def policySARSA(mdp, epsilon, alpha, iterNum, maxWalkLen=100, echoSE=False):
"""
Actor-Critic: actor update the policy, critic update the value.
"""
InitParameter = 0.1
if echoSE:
squareErrors = []
policy = SoftmaxPolicy(mdp)
valuePolicy = Policy(mdp)
for i in range(len(policy.parameters)):
policy.parameters[i] = InitParameter
valuePolicy.parameters[i] = 0.0
for _ in range(iterNum):
if echoSE:
squareErrors.append(getSquareErrorPolicy(valuePolicy))
state = random.choice(mdp.states)
sFeature = mdp.getFeature(state)
action = random.choice(mdp.actions)
isTerminal = False
count = 0
while not isTerminal and count < maxWalkLen:
isTerminal, nextState, reward, nextSFeature = mdp.transform(state, action)
nextAction = policy.epsilonGreedy(nextSFeature, epsilon)
valuePolicy.update(sFeature, action,
reward + mdp.gamma * valuePolicy.qFunc(nextSFeature, nextAction), alpha)
policy.update(sFeature, action, valuePolicy.qFunc(sFeature, action), alpha)
sFeature = nextSFeature
action = nextAction
count += 1
if echoSE:
return policy, squareErrors
else:
return policy
def featureQLearning(mdp, epsilon, alpha, iterNum, maxWalkLen=100, echoSE=False):
"""
qFunc = r + max_{a'}(\gamma * q(\hat{s'}, a'))
"""
InitParameter = 0.1
if echoSE:
squareErrors = []
policy = Policy(mdp)
for i in range(len(policy.parameters)):
policy.parameters[i] = InitParameter
for _ in range(iterNum):
if echoSE:
squareErrors.append(getSquareErrorPolicy(policy))
state = random.choice(mdp.states)
sFeature = mdp.getFeature(state)
action = random.choice(mdp.actions)
isTerminal = False
count = 0
while not isTerminal and count < maxWalkLen:
isTerminal, nextState, reward, nextSFeature = mdp.transform(state, action)
maxQ = -1.0
for nextAction in mdp.actions:
q = policy.qFunc(nextSFeature, nextAction)
if maxQ < q:
maxQ = q
policy.update(sFeature, action, reward + mdp.gamma * maxQ, alpha)
action = policy.epsilonGreedy(nextSFeature, epsilon)
state = nextState
sFeature = nextSFeature
count += 1
if echoSE:
return policy, squareErrors
else:
return policy
